Where do we stand in terms of understanding inequalities in health system of Nepal? What is being done?

The Constitution of Nepal (2015) provides greater inclusion of female, marginalized and disadvantaged groups [18, 33]. Subsequently, there has been notable progress in biological and the social construct of gender approaches in various policies and strategies. These initiatives mandate civil society and economic participation, as well as health service utilisation by women. Gender, social inclusion and the concept of intersectionality are well incorporated into existing National Health Policy, Nepal Health Sector Strategy, Gender Equality and Social Inclusion Strategy of the Health Sector, Urban Health Policy, Population Policy and National Strategy for Reaching the Unreached [19,20,21, 23, 24].

In an endeavor to reach the unreached, the Ministry of Health and Population (MoHP) established a 'Gender Equality and Social Inclusion' (GESI) section in 2013. This proactive step aimed to address disparities and promote inclusivity by mainstreaming GESI in the health sector [34]. However, despite numerous efforts, the implementation of GESI policies faces challenges due to limited operational structures and capacity at various levels within the health system. Consequently, inequities in health outcomes persist across various social stratifiers [3, 35]. Challenges continue with the implementation of gender-sensitive and gender-responsive legislation, policies, and acts, including the intersectional recognition of factors affecting men or women based on ethnicity, caste, religion, language, indigeneity, marital status, occupation, geographical location, ability, and access to health and education [34, 36,37,38]. These interaction occur within connected systems where social determinants and the structure of power in the society synergistically and antagonistically act, forming the privilege and oppression of individuals [39].

Furthermore, in 2014/15, the MoHP revised the HMIS to include variables such as sex, age, caste/ethnicity, and location/address. This revision enables the assessment of disaggregated health data, offering a more comprehensive understanding of 11 selected health indicators [34, 40]. HMIS is primarily used in the public sector for recording and reporting routine health services data from public health facilities at all three levels of government (local, provincial, and federal). The private sector maintains its own information systems for recording purposes, which are not yet integrated with the government's HMIS. However, a few private health facilities report to HMIS for selected programme indicators only.

Health Management Information System (HMIS): TB as a case example

HMIS in Nepal comprises distinct registers for recording TB service data, namely HIMS 6.1 Tuberculosis Sample Collection Form, 6.2 Tuberculosis Laboratory Register, 6.3 Tuberculosis Treatment Card (Health Facility), 6.4 Tuberculosis Treatment Card (Patient), 6.5 Tuberculosis Treatment Register, 6.6 Smoking cessation Register, 6.7 drug resistant (DR) Tuberculosis Laboratory Register, and 6.8 DR Tuberculosis Treatment Register [26]. All these TB registers typically include fields for recording demographic information, including age, sex, ethnicity, address, name of the caregivers and contact number of the service recipients. The classification of sex is limited to male and female, with no provision for individuals with non-binary gender identities.

While social stratifiers such as age, sex and ethnicity are recorded at the health facility levels, there are limitations in reporting this data to higher authorities. The standard reporting format predominantly focus on sex and age-disaggregated data. Information disseminated at the national level by the government through annual reports based on HMIS findings includes disaggregation by sex, age, and province. This highlights the gap, indicating that the health information system has limitations in understanding service utilisation patterns by different population groups to make tailored decisions and interventions (Fig. 1).

Fig. 1

Flowchart presenting loss of variables during recording and reporting mechanism of TB. DHIS2 District Health Information Software 2; DoHS Department of Health Services; HMIS Health management information system

Scope of conducting disaggregated and intersectional analysis from the available HMIS data: taking TB as an example

Secondary data analysis was performed to assess the current limitations in conducting intersectional gender analysis with the available TB data through the HMIS, rather than producing new findings to inform disease (TB) perspective. It is essential to note that the TB programme is taken only as an illustrative example. The insights gained from this analysis could contribute to inform HMIS recording and reporting practices for various diseases and health programmes, promoting a more inclusive system.

Trend of annually reported TB cases disaggregated by ecological region, age and sex

There were pronounced variations in TB cases across different regions of Nepal. The Terai region (the lowland plains) consistently reported the highest TB cases, followed by the Hill region (the hilly areas) and the Mountain region (the mountainous areas) for the last five years. The highest proportion of TB cases was found among the population aged 65 years and above, whereas lowest proportion was found among less than 14 years. In terms of sex-wise distribution, the proportion of TB cases is notably higher among males compared to females over the last five years. These findings provide important insights into the epidemiology of TB in Nepal, showcasing variations in regional prevalence, age-related patterns, and gender disparities (Fig. 2).

Fig. 2

Tuberculosis cases by region, age, sex (Data Source-National Tuberculosis Control Center) [41]

Disaggregated analysis of the recorded TB cases

We collected information from 628 TB patients from two DOTS centers, among whom 510 (81.2%) were new TB patients, while 118 (18.8%) had received previous TB treatment. During the data collection period, 152 (24.2%) were under TB DOTS treatment and 476 (75.8%) had completed their treatment. Among the patients, 338 (54.0%) had pulmonary TB (PTB), and 290 (46.0%) had extra-pulmonary TB (EPTB). Of those who completed treatment, 399 (83.8%) were successfully treated, 71 (14.9%) had an unfavorable treatment outcome and 6 (1.3%) moved to second line treatment (data not shown).

The overall male-to-female TB patient ratio was 1.1 (333/295). The age distribution of male TB patients ranged widely from a minimum age of 9 months to a maximum age of 92 years. Similarly, the age diversity of female TB patients followed a similar pattern, ranging from a minimum age of one year to a maximum age of 93 years. However, median (md) and inter-quartile range (IQR) for the age of males (md = 34 years; IQR = 22–50) were higher than those of females (md = 27 years; IQR = 21–38). In both sexes, the highest percentage of TB patients belonged to the 25–54 years age group [male (46.6%) and female (45.4%)], while the ≤ 14 years age group had the lowest TB cases [male (5.7%) as well as female (4.4%)]. Similarly, more than half of the male (55.6%) and female (56.3%) TB patients belonged to advantaged caste group, while the remaining belonged to disadvantaged caste group (Table 1).

Table 1 Socio-demographic characteristics of the study participantsComparison of types of TB according to age, sex and ethnicity

There was a significant association between sex of the patient and the types of TB (P < 0.05). Among the reported cases, the proportion of males with PTB was higher (61.3%) compared to females (45.4%), while the proportion of males with EPTB was lower (38.7%) than that of females (54.6%). Figure 3 shows the proportion of pulmonary TB patients and their 95% confidence interval among different age and ethnic groups disaggregated by sex. The red horizontal line in Fig. 3 represents the proportion of pulmonary TB among total cases, i.e., 54.0%. Within males, the proportion of PTB increased with age, with the highest proportion of TB patients observed in the ≥ 55 years age group. Males had a higher prevalence of PTB compared to females in both, advantaged and dis-advantaged caste group (Fig. 3).

Fig. 3

Comparison of Pulmonary TB cases by age and ethnic groups disaggregated by sex

Patient’s registration category (old/new cases) across age, sex and ethnicity

Age and sex were significantly associated with patients’ types of TB cases during registration while enrolling into the TB regimen (P < 0.05). A significantly higher percentage of males (61.9%) sought retreatment compared to females (38.1%) (P < 0.05). Similarly, patients in the 25–54 years age group constituted a significantly higher proportion (44.1%) in the retreatment category. Although not statistically significant, a higher proportion (59.3%) of the disadvantaged caste group sought retreatment compared to the advantaged caste group (40.7%). While the difference is not statistically significant, it still underscores a noteworthy trend. (Table 2).

Table 2 Sex, age and ethnic groups by types of cases (old/new) during registration at DOTS centerTreatment outcome across age, sex and ethnicity

Out of 628 TB patients, a treatment outcome was obtained for 470 patients and 6 patients were moved to the second line treatment, which was not considered in the two categories of treatment outcome (successful and unfavorable) [32]. Figure 4 demonstrates the successful treatment outcome and its 95% confidence interval among age groups and ethnic groups disaggregated by sex. The red horizontal line represents the proportion of treatment success of TB patients among total TB patients i.e., 84.8%. The rate of successful treatment gradually decreased with age among both male and female TB patients. Female TB patients had higher successful treatment outcome in comparison to male across both caste groups.

Fig. 4

Comparison of treatment success rate of TB cases by age and ethnic groups disaggregated by sex

Multivariate logistic analysis was conducted to assess the relationship between combined variables i.e. ‘sex and age’ and ‘sex and ethnicity’ and treatment outcome, where age group was categorized into two groups (≤ 25 years and > 25 years) due to insufficient sample size within four category of age groups. The results reveal that male more than 25 years exhibited higher odds (aOR = 4.95, 95% CI: 1.60–19.06, P = 0.01) of successful outcome compared to male TB patients less than 25 years (Table 3).

Table 3 Multivariate analysis between sex, age and ethnicity and treatment outcome