Socioeconomic inequalities in effective service coverage for reproductive, maternal, newborn, and child health: a comparative analysis of 39 low-income and middle-income countries.

BACKGROUND: Reducing socioeconomic inequalities in access to good quality health care is key for countries to achieve Universal Health Coverage. This study aims to assess socioeconomic inequalities in effective coverage of reproductive, maternal, newborn and child health (RMNCH) in low- and middle-income countries (LMICs). METHODS: Using the most recent national health surveys from 39 LMICs (between 2014 and 2018), we calculated coverage indicators using effective coverage care cascade that consists of service contact, crude coverage, quality-adjusted coverage, and user-adherence-adjusted coverage. We quantified wealth-related and education-related inequality using the relative index of inequality, slope index of inequality, and concentration index. FINDINGS: The quality-adjusted coverage of RMNCH services in 39 countries was substantially lower than service contact, in particular for postnatal care (64 percentage points [pp], p-value<0·0001), family planning (48·7 pp, p<0·0001), and antenatal care (43·6 pp, p<0·0001) outcomes. Upper-middle-income countries had higher effective coverage levels compared with low- and lower-middle-income countries in family planning, antenatal care, delivery care, and postnatal care. Socioeconomic inequalities tend to be wider when using effective coverage measurement compared with crude and service contact measurements. Our findings show that upper-middle-income countries had a lower magnitude of inequality compared with low- and lower-middle-income countries. INTERPRETATION: Reliance on the average contact coverage tends to underestimate the levels of socioeconomic inequalities for RMNCH service use in LMICs. Hence, the effective coverage measurement using a care cascade approach should be applied. While RMNCH coverages vary considerably across countries, equitable improvement in quality of care is particularly needed for lower-middle-income and low-income countries. FUNDING: None.

Impact of non-communicable disease multimorbidity on health service use, catastrophic health expenditure and productivity loss in Indonesia: a population-based panel data analysis study.

OBJECTIVES: To examine non-communicable diseases (NCDs) multimorbidity level and its relation to households' socioeconomic characteristics, health service use, catastrophic health expenditures and productivity loss. DESIGN: This study used panel data of the Indonesian Family Life Survey conducted in 2007 (Wave 4) and 2014 (Wave 5). SETTING: The original sampling frame was based on 13 out of 27 provinces in 1993, representing 83% of the Indonesian population. PARTICIPANTS: We included respondents aged 50 years and above in 2007, excluding those who did not participate in both Waves 4 and 5. The total number of participants in this study are 3678 respondents. PRIMARY OUTCOME MEASURES: We examined three main outcomes; health service use (outpatient and inpatient care), financial burden (catastrophic health expenditure) and productivity loss (labour participation, days primary activity missed, days confined in bed). We applied multilevel mixed-effects regression models to assess the associations between NCD multimorbidity and outcome variables, RESULTS: Women were more likely to have NCD multimorbidity than men and the prevalence of NCD multimorbidity increased with higher socioeconomic status. NCD multimorbidity was associated with a higher number of outpatient visits (compared with those without NCD, incidence rate ratio (IRR) 4.25, 95% CI 3.33 to 5.42 for individuals with >3 NCDs) and inpatient visits (IRR 3.68, 95% CI 2.21 to 6.12 for individuals with >3 NCDs). NCD multimorbidity was also associated with a greater likelihood of experiencing catastrophic health expenditure (for >3 NCDs, adjusted OR (aOR) 1.69, 95% CI 1.02 to 2.81) and lower participation in the labour force (aOR 0.23, 95% CI 0.16 to 0.33) compared with no NCD. CONCLUSIONS: NCD multimorbidity is associated with substantial direct and indirect costs to individuals, households and the wider society. Our study highlights the importance of preparing health systems for addressing the burden of multimorbidity in low-income and middle-income countries.

The Joint Effect of Physical Multimorbidity and Mental Health Conditions Among Adults in Australia.

INTRODUCTION: The prevalence of chronic physical and mental health conditions is rising globally. Little evidence exists on the joint effect of physical and mental health conditions on health care use, work productivity, and health-related quality of life in Australia. METHODS: We analyzed data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey, waves 9 (2009), 13 (2013), and 17 (2017). Economic effects associated with multimorbidity were measured through health service use, work productivity loss, and health-related quality of life. We used generalized estimating equations to assess the effect of the association between physical multimorbidity and mental health conditions and economic outcomes. RESULTS: From 2009 through 2017 the prevalence of physical multimorbidity increased from 15.1% to 16.2%, and the prevalence of mental health conditions increased from 11.2% to 17.3%. The number of physical health conditions was associated with the number of health services used (general practitioner visits, incidence rate ratio = 1.41), work productivity loss (labor force participation, adjusted odds ratio = 0.71), and reduced health-related quality of life (SF-6D score: Coefficient = -0.03). These effects were exacerbated by the presence of mental health conditions and low socioeconomic status. CONCLUSION: Having multiple physical health conditions (physical multimorbidity) creates substantial health and financial burdens on individuals, the health system, and society, including increased use of health services, loss of work productivity, and decreased health-related quality of life. The adverse effects of multimorbidity on health, quality of life, and economic well-being are exacerbated by the co-occurrence of mental health conditions and low socioeconomic status.