Health equity impact of community-initiated kangaroo mother care: A randomized controlled trial

doi:10.21203/rs.3.rs-770150/v1

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Health equity impact of community-initiated kangaroo mother care: A randomized controlled trial

https://doi.org/10.21203/rs.3.rs-770150/v1

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published 01 Dec, 2021

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Background

Kangaroo mother care (KMC) can substantially enhance overall survival of low birthweight babies. In a large randomized controlled trial, we recently showed that supporting mothers to provide community initiated KMC (ciKMC) can reduce early infant mortality by 25% (hazard ratio (HR) 0.75). With the current analysis we aimed to explore if ciKMC promotion leads to increased inequity in survival.

Methods

In the trial we randomised 8402 low birthweight babies to a ciKMC (4480 babies) and a control (3922 babies) arm, between 2015 and 2018 in Haryana, India. The main outcome of interest was the difference in concentration indices between babies in the ciKMC and control arms for survival until 180 days of life.

Results

Our intervention did not increase survival inequity, as reflected in a reduction of the concentration index by 0.05 (95% CI -0.07 to 0.17). Survival impact tended to be higher among those born to illiterate mothers (HR 0.65, 95% CI 0.47 to 0.89), and those in poor families (HR 0.69, 95% CI 0.51 to 0.93).

Conclusions

We found that ciKMC promotion did not increase inequity in survival associated with wealth. The beneficial impact of ciKMC tended to be larger among vulnerable groups. Supporting mothers to provide KMC at home to low birthweight babies will not increase and could indeed reduce inequities in infant survival.

Trial registration

ClinicalTrials.gov, NCT02653534. Registered January 12, 2016 - Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT02653534

Health Policy

Kangaroo Mother Care

community

neonatal

infant

equity

randomized controlled trial

Health inequality refers to differences in the distribution of health status across population groups. When these differences are preventable and unjust across socially relevant groupings, it leads to health inequity [1]. Reducing inequities in health is a widely endorsed policy goal; however, the empirical evidence base for how to achieve such a reduction is weak. Interest is now shifting from only describing social determinants of health to understanding how these inequities can be addressed using programmatic and policy interventions [2]. Randomized controlled trials (RCTs) typically report average efficacy, and there is a lack of high-quality RCTs describing the impact of interventions on inequities between individuals or groups. Rigorous methods for measuring equity impact on health using an RCT design are needed, as data from RCTs are used to inform policy decisions [2–4].

It is important to investigate whether efficacious interventions lead to inequities. Such information is useful for those deciding on the efficient and equitable allocation of healthcare resources. It could also provide input to economic evaluations, such as distributional cost-effectiveness analysis (DCEA) [5]. There are at least three different ways in which an intervention can be shown to have equity impact in RCTs. First, if an effective intervention targets the most disadvantaged subgroup, improvement in health outcomes will have a positive impact on health equity. Second, if the intervention aims to improve access to health services of known effectiveness, improved access for disadvantaged groups over groups that are better off will also increase equity. Finally, if the health impact is differential across groups defined by equity-relevant characteristics like wealth, education, caste, race and gender, there may be a positive impact on equity if disadvantaged groups benefit more than those better off. On the contrary, if an intervention is of greater benefit to advantaged groups than to disadvantaged groups, intervention-generated inequalities may arise.

In general, lower socioeconomic status, poverty, low levels of education and residence in rural or remote areas are all associated with poor health [6]. In India, girls overall, experience poorer health than boys and people belonging to socially disadvantaged castes experience poorer health than others [7, 8]. Low birthweight infants (birthweight < 2,500 g), especially in resource-poor settings, have a much higher mortality and morbidity than infants with a normal birthweight [9]. In a recent RCT in Haryana, India, we found that active promotion of community-initiated kangaroo mother care (ciKMC) for infants weighing 1500 to 2250 g within 72 h of being born reduced their mortality until 180 days of life by 25% (95% CI: 7–40%) [10].

Here, we define health inequity as an unequal and unfair distribution of health benefits across strata defined by socioeconomic status, caste, level of education of the mother or gender of the infant, whereas a positive equity impact is reflected in a reduction in unequal and unfair occurrence of health outcomes in infants who receive the intervention compared to those who do not [2, 11]. In this study, we explored the impact of ciKMC on the distribution of improvements in survival across wealth status, mother’s education, caste, religion, and infant gender. We hypothesized that ciKMC would not lead to unfair intervention-generated inequities by disproportionately benefitting advantaged groups.

Adhering to the CONSORT-Equity 2017 reporting standards, we collected our data in Faridabad and Palwal district of Haryana in India between 30 July 2015 and 31 October 2018 as part of the individually randomized controlled parallel-arm ciKMC trial [2, 10, 12]. Earlier studies done in this area show substantial health inequity across characteristics like caste, gender, maternal literacy, and wealth status [8, 12, 13]. Our formative research facilitated capturing the characteristics of the PROGRESS-Plus framework, including caste, gender, religion, education, and socioeconomic status [14, 15].

The main effect estimate of the current study was the difference in concentration index between infants in the ciKMC and control arms for post enrolment survival until 180 days of life. We also compared the hazard ratios of post enrolment survival until 180 days of life between the infants in the ciKMC and the control arms across wealth status, mother’s education level, family’s caste, family’s religion, and infant’s gender.

The field team assessed the infants at home and weighed them as soon as possible (no later than 72 hours) after birth; they were eligible if they weighed between 1500 and 2250 g [10]. Infants with an inability to feed, difficulty in breathing, less than normal movements or with gross congenital malformation; those for whom KMC had been initiated in hospitals; and infants whose mothers planned to move out of the study area during the trial period were excluded.

The intervention consisted of the newborns being kept in skin-to-skin contact with their mother or a surrogate and exclusively breastfed for as long as possible. An intervention delivery team made nine home visits in the intervention arm during the first 28 days of life to support KMC. No intervention was given to the control families but families in both the intervention and control arms of the trial continued to receive routine home-based care from the public health system. We collected socioeconomic and demographic data at baseline. During regular home visits, a separate team of well-trained research assistants, masked to trial-arm allocation, collected the vital status of the participating babies until they were six months of age. The data collection procedures were identical in both arms.

Descriptive statistics with summary measures of health inequality

We used principal component analysis (PCA) to calculate an asset index score, which we then used to rank the study participants. We calculated the asset index, using data on household ownership of selected assets (e.g., televisions and bicycles), the materials used for housing construction and the types of water access and sanitation facilities. The method we used to generate the asset index was similar to that used by the Demographic and Health Survey Program [16]. The wealth status of the lower 40% of the study population based on asset index score (i.e., representing the two lowest quintiles) was categorized as poor – the upper three quintiles were categorized as non-poor [17].

We present the study outcomes by wealth quintile to explore social gradients in the two trial arms. To investigate, summarize and draw inferences about the impact of the intervention on health inequity, we used concentration curves, concentration indices and the difference in the concentration indices between the two arms [18]. We used an F-test to estimate the statistical precision of this difference. A positive difference reflects a positive equity impact and negative difference indicates higher inequity [19]. We used Stata 16.1 (StataCorp LLC, College Station, Texas) and community-contributed commands (“conindex”, “Lorenz”, “ic”) for our analyses [18, 20, 21].

Inferential analysis

We calculated the hazard ratios (HRs) across the subgroups defined by wealth status [non-poor vs poor], infant’s gender, family caste [scheduled caste (SC)/scheduled tribe (ST)/other backward caste (OBC) vs other], religion [Hindu vs other] and mother’s literacy [illiterate vs literate], between the two trial arms using Cox regression models for time until death up to 180 days of life. The analyses accounted for clustering of events among infants within the same household using robust standard errors. We estimated the biologic interaction (i.e., interaction assessed on the additive scale) using the relative excess risk due to interaction (RERI) for wealth status, infant’s gender, caste, religion, and mother’s literacy status [22].

Randomisation successfully balanced important PROGRESS-Plus characteristics between the trial arms (Table 1).

Table 1

Baseline characteristics of the participants in the ciKMC trial across relevant PROGRESS Plus characteristics.
Characteristic	ciKMC (n = 4480) number (%)	Control (n = 3922) number (%)
Family wealth status ^a Non-poor Poor	2714 (61) 1761 (39)	2323 (59) 1599 (41)
Family caste Scheduled caste/tribe (SC/ST) Other backward caste (OBC) Other	1573 (35) 1947 (44) 955 (21)	1305 (33) 1777 (45) 839 (21)
Gender Male Female	1907 (43) 2573 (57)	1741 (44) 2181 (56)
Mother never been to school ^a	1625 (36)	1341 (34)
Religion ^a Hindu Other	3653 (82) 827 (18)	3195 (82) 727 (18)
Mean (SD) Age of mother years ^a	23.3 (3.7)	23.4 (3.8)
Mother working outside of home ^a	226 (5.0)	223 (5.7)
^a Information was not available for five infants in intervention group. SD – Standard deviation

There was a clear social gradient with higher mortality in the first six months of life in the poorest compared to the least poor quintile (Fig.1a).

Figure 1a) Mortality risk by wealth quintile from enrolment to 180 days of life in the control arm, 1b) Concentration index for mortality from enrolment to 180 days of life in the control arm.

From the observed distribution (blue curve in Fig.1b), we can see that 50% of the mortality was concentrated among the poorest third of the study participants. In general, mortality was concentrated among the poorer families, reflected in the distribution curve lying above the diagonal line of equality (black dashed straight curve). While a concentration index of 0.0 would have reflected a perfectly equal distribution of mortality by wealth status, the observed concentration index of -0.17 shows that mortality was concentrated among the poorer participants.

Figure 2a) Mortality risk by wealth quintile for mortality from enrolment to 180 days of life in the control and intervention arm, 2b) Concentration index for mortality from enrolment to 180 days of life in the control and intervention arm.

We assessed the equity impact of the intervention on mortality by comparing the distributions with the corresponding distributions in the intervention arm (Fig. 2a and 2b). Mortality from enrolment to 180 days of life was lower in the intervention arm compared to in the control arm for all quintiles, that difference being most pronounced for the poorest quintile (Fig. 2a). The mortality in the intervention arm (solid red curve in Fig. 2b) was still concentrated among the poorest families, but to a lesser degree than in the control arm. The concentration index in the intervention arm was − 0.12, i.e., 0.05 (95% CIs − 0.07 to 0.17) lower than that in the control arm. Hence, ciKMC did not increase inequity in early infant survival.

Impact of the intervention across subgroups

Table 2 contains the comparisons between the ciKMC and the control arm HRs for death from enrolment to 180 days of life across socioeconomic status (poor vs non-poor), caste (SC/ST/OBC vs other), maternal literacy (illiterate vs literate), gender (boy vs girl) and religion (Hindu vs other). ciKMC promotion substantially reduced mortality in all the disadvantaged subgroups. The HRs were lower, i.e., the effect of ciKMC promotion seemed to be higher, among the disadvantaged groups than among the advantaged groups. Thus, infants in the intervention arm born in poor families had a lower HR than those in non-poor families (0.69 vs 0.84). Likewise, mortality saw a greater decrease with the intervention in disadvantaged castes versus in other castes (HR: 0.72 vs 0.95), among infants born to illiterate vs literate mothers (HR: 0.65 vs 0.85) and among female vs male infants (HR: 0.73 vs 0.78). Still, the 95% CIs for the RERIs were relatively wide. The positive – albeit statistically not very precise – interaction between the PROGRESS Plus subgroups and allocation to the ciKMC trial arm suggests that the effect of the impact of ciKMC may be greater in disadvantaged groups (Table 2).

Table 2

Hazard ratio (95% CI) and relative excess risk due to interaction (RERI) (95% CI) for death between enrolment to day 180 of life in the intervention and control arm of the ciKMC triala across religion, caste, maternal literacy, infant’s gender, and socioeconomic status.
Mortality from enrolment to 180 days
		Control	ciKMC	HR (95% CI)	RERI (95% CI)
Socio-economic status ^b	Poor	103 (1596)	79 (1754)	0.69 (0.51 to 0.93)	0.23 (-0.05 to 0.50)
Socio-economic status ^b	Non-poor	81 (2318)	79 (2711)	0.84 (0.61 to 1.15)	0.23 (-0.05 to 0.50)
Caste	SC ^c/ST ^c/OBC ^d	155 (3075)	127 (3510)	0.72 (0.56 to 0.91)	0.25 (-0.14 to 0.63)
Caste	Other	29 (838)	31 (955)	0.95 (0.56 to 1.60)	0.25 (-0.14 to 0.63)
Maternal literacy	Illiterate	89 (1337)	71 (1619)	0.65 (0.47 to 0.89)	0.27 (0.01 to 0.54)
Maternal literacy	Literate	94 (2573)	87 (2844)	0.85 (0.63 to 1.14)	0.27 (0.01 to 0.54)
Infant gender	Female	103 (2177)	89 (2567)	0.73 (0.54 to 0.97)	0.06 (-0.31 to 0.43)
Infant gender	Male	81 (1737)	69 (1903)	0.78 (0.56 to 1.09)	0.06 (-0.31 to 0.43)
Religion	Other	46 (721)	35 (817)	0.65 (0.42 to 1.02)	0.2 (-0.15 to 0.56)
Religion	Hindu	138 (3191)	123 (3648)	0.78 (0.61 to 1.01)	0.2 (-0.15 to 0.56)
^a Data for different characteristics were missing for < 30 (0.4%) of the participants.
^b Lower two wealth quintiles have been categorised as poor and the top three wealth quintiles have been categorised as non-poor.
^c Scheduled castes (SCs), and scheduled tribes (STs) are officially designated groups of historically disadvantaged people in India.
^d Other backward caste (OBC) is a collective term the Government of India uses to classify castes that are educationally or socially disadvantaged.

We found that ciKMC provided to low birthweight infants (1500 to 2250 g) did not seem to increase inequity in survival across categories of wealth, socioeconomic status, caste, mother’s education, infant’s gender, or religious group. If anything, promotion of ciKMC reduced inequity, as illustrated by the reduction in the concentration index and the fact that the effect of ciKMC on survival appeared to be greater among infants from poor households, infants born to illiterate mothers and infants from disadvantaged castes. Still, the CIs were generally too wide to conclude that ciKMC promotion reduces inequity.

To our knowledge, no other large equity relevant RCTs have been conducted on KMC. This study is the first and the largest RCT assessing the mortality impact of KMC initiated in communities where low birthweight is very common. Less than 0.03% of the participating infants were lost to follow-up. The only other RCT on ciKMC that has been conducted did not explore the consistency of impact across potential axes of socio-economic differences [23]. The Cochrane Review on the effect of hospital initiated KMC was updated in 2016; however, subgroup analyses and other analyses to examine possible negative equity impact were not presented [24].

A limited number of studies have directly estimated the equity impact of interventions using individual-level data from randomized trials. We applied the classical measures of health equity and added a direct measure of inequity impact using the difference in concentration index along with a regression-based analysis of biologic interaction to assess the equity impact [18, 20, 22]. The indices illustrate the distribution of intervention health impact.

While it is comforting that promotion of ciKMC did not seem to induce inequity in infant survival, there are several reasons to expect that ciKMC in fact can reduce it. For example, a reduction in inequity is consistent with findings that providing access to healthcare close to home reduces health-related inequities [25]. Lack of education among mothers has been associated with reduced awareness regarding the importance of exclusive breastfeeding [26]. We believe that the very close physical contact between baby and mother could supersede any tendencies of suboptimal breastfeeding practices which may be more common among women with less education. Further, KMC has been credited with empowering mothers and increasing their self-efficacy to take better care of their infants [23]. Finally, as in other parts of the world, the proportion of children born with low birthweight is higher in disadvantaged groups in the study area [8, 27]. Many parents in these groups have limited awareness of appropriate newborn care and have poor access to facility-based neonatal care, which is often unaffordable for them. Hence, ciKMC could have a higher impact on survival in these groups than in advantaged groups.

We used an asset index to rank individuals to assess the inequality in health outcomes; and the caution that applies to such assessment should be exercised in interpreting our findings [28, 29]. Despite the large sample size (n = 8402), the trial was not primarily powered to assess the differential impact of the intervention across axes of disadvantage. Still, the large trial enabled us to state that ciKMC promotion and support did not enhance pre-existing inequities in young infant survival. Low birthweight was common in the trial setting, and caution must be exercised in extrapolating the findings to settings with high neonatal mortality but a lower prevalence of low birthweight.

Limited statistical precision will be an important issue for future studies on equity impact of healthcare interventions targeting important but rare outcomes like death. In addition to an intervention being offered to everyone, there may be a need to target those known to be particularly vulnerable to undesirable outcomes and/or who are especially resistant to adequate uptake or responding to an intervention. Although outcomes with higher incidence/prevalence would be more suitable for such studies, equity impact analyses for rare outcomes of clear importance such as those addressed in the current study are valuable – they would convey whether potential intervention-driven inequities are generated or not.

In addition to substantially reducing mortality, promoting ciKMC did not induce a discernible inequity in survival-related to wealth, caste, maternal literacy, infant gender or religion between enrolment and 180 days of life. The intervention placed little demand on family resources beyond the mother’s time; it did not require any equipment or complex knowledge of technical skills; and it was culturally appropriate – these are all characteristics that facilitated the intervention adoption by poorer and less-educated families. Supporting mothers to provide KMC at home to low birthweight babies will not increase and could indeed reduce inequities in infant survival.

RCTs - Randomized controlled trials

DCEA - Distributional Cost-Effectiveness Analysis

ciKMC – Community Initiated Kangaroo Mother Care

CONSORT - Consolidated Standards of Reporting Trials

PCA - Principal Component Analysis

HR – Hazard Ratio

SC – Scheduled Caste

ST – Scheduled Tribe

OBC – Other Backward Caste

RERI - Relative Excess Risk due to Interaction

CI – Confidence Interval

Ethics approval and consent to participate

A trained interviewer obtained informed consent as previously described [10]. The study was registered with ClinicalTrials.gov, NCT02653534. We obtained ethics approval from the ethics committees of the Society for Applied Studies in India and the Regional Committee for Medical and Health Research Ethics in Norway.

Consent for publication

All authors reviewed the manuscript and gave consent for publication.

Availability of data and materials

The dataset pertaining to the results reported in the manuscript will be made available to others only for health and medical research, subject to constraints of the consent under which the data was collected. De-identified individual participant data will be made available along with the data dictionary, study protocol, and informed consent form. Data will be available beginning 12 months and ending 5 years after publication of this article. Requests for data should be made to Dr Tarun Shankar Choudhary ([email protected]). The requester should provide a methodologically sound secondary research proposal, approved by an independent review committee. The requester must be able to show their ability to carry out the proposed use of the requested dataset through their peer review publications and declare conflicts of interest in relation to the requested dataset and their funding sources. The authors reserve the right to refuse sharing of data in the face of potential adversarial conflicts of interest. A Data Sharing Agreement that meets the data sharing requirements of the Society for Applied Studies (New Delhi, India) and Centre for International Health, University of Bergen (Norway) will be signed with the data requester. Data must only be used for the purpose described in the secondary research proposal as further stipulated in the Data Sharing Agreement. Data will be transferred only to requesters named in the original proposal and as specified in the relevant Data Sharing Agreement.

Competing interests

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Funding

This trial was supported by the Research Council of Norway through its Centres of Excellence Scheme (grant number 223269) and by the University of Bergen through funding to the Centre for Intervention Science in Maternal and Child Health. The funding entities played no role in the study design, data collection, data analysis, data interpretation or writing of the manuscript.

Authors' contributions

All authors contributed substantially to the conception or design of the study or to the data acquisition, analysis, or interpretation. TSC, SM, ST were responsible for the day-to-day implementation of the study, data management, and analysis. TSC, OAH and HS performed the statistical analysis. TSC, OFN, OAH, KAJ, HS and SM drafted the manuscript. NB, RB, JM, MKB, and HS provided technical support, monitoring, and coordination for the trial. All authors critically revised the manuscript for important intellectual content. TSC is the corresponding author and attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. The manuscript was reviewed and has final approval from all authors.

Acknowledgements

We acknowledge the contribution and support of the mothers and families of participating children. The Society for Applied Studies acknowledges the core support provided by the Department of Maternal, Newborn, Child and Adolescent Health, World Health Organization, Geneva (WHO Collaborating Centre IND‐158); the Centre for Intervention Science in Maternal and Child Health, Centre for International Health, University of Bergen (Norway); and Knowledge Integration and Translational Platform (KnIT), a Grand Challenges Initiative of the Biotechnology Industry Research Assistance Council (BIRAC), Department of Biotechnology, Government of India and Bill & Melinda Gates Foundation (USA).

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Health equity impact of community-initiated kangaroo mother care: A randomized controlled trial

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