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Abstract

Introduction

Pediatric acute leukemia is the most common childhood malignancy and one of the leading causes of cancer-related mortality worldwide, particularly, in low- and middle-income countries (LMICs), where treatment abandonment remains a major barrier to survival. Geographic accessibility and socioeconomic conditions are recognized determinants, but their combined influence in Mexico remains understudied. This study evaluated the association between geographic accessibility, socioeconomic factors, and treatment abandonment among children with acute leukemia in south-central Mexico.

Methods

A prospective cohort study was conducted in Oaxaca, Puebla, and Tlaxcala from 2021 to 2023, including 574 children under 18 years diagnosed with acute lymphoblastic or myeloid leukemia. Geographic accessibility was estimated using travel distance and time from patients’ residences to referral hospitals, calculated with ORS Tools in QGIS. Socioeconomic variables included public health insurance affiliation, parental education and occupation, and number of siblings. Treatment abandonment was defined per SIOP criteria as failure to initiate or discontinuation of treatment for ≥4 consecutive weeks. Multivariable logistic regression, adjusted for child’s sex, age, year of diagnosis, and leukemia subtype, was used to assess associations.

Results

Treatment abandonment occurred in 16.6% of patients. In multivariable analysis, lack of public health insurance (aOR = 2.83; 95% CI: 1.39-5.76; P < 0.01) and living ≥141 km from the hospital (aOR = 1.68; 95% CI: 1.02-2.74; P = 0.03) were significantly associated with abandonment. Other factors, including number of siblings, maternal education, and fathers’ occupation, were not statistically significant.

Conclusion

Lack of public health insurance and greater distance to the hospital are key determinants of treatment abandonment in children with acute leukemia in south-central Mexico. Expanding insurance coverage, reducing indirect costs, and addressing geographic barriers are critical to improve treatment adherence and survival outcomes in this population.

Plain Language Summary

Leukemia is the most common type of cancer in children. Today, many children can be cured if they receive full treatment on time. However, in some places, children do not finish their treatment. This is called “treatment abandonment,” and it is one of the main reasons why children die of leukemia in low- and middle-income countries. In this study, we followed 574 children under 18 years old with leukemia in Oaxaca, Puebla, and Tlaxcala between 2021 and 2023. We looked at two main factors: how far families live from the hospital and their socioeconomic situation, including whether they had public health insurance. We measured the distance from the children’s homes to the hospitals and collected information about parents’ education, work, and family size. We found that about 17 out of every 100 children (16.6%) abandoned their treatment. Two main reasons explained this: not having public health insurance and living far away from the hospital. Children without health insurance were almost three times more likely to stop treatment, and those living more than 141 kilometers away were about 1.7 times more likely to abandon it. Other family factors, such as the number of siblings or parents’ education level, did not have a strong impact. These results show that distance and lack of insurance are the biggest barriers to completing leukemia treatment in this region of Mexico. To save more lives, it is important to expand access to health insurance, reduce the hidden costs of care, and find ways to support families who live far from hospitals.

Keywords

pediatric leukemia treatment abandonment geographic accessibility public health insurance socioeconomic factors

Introduction

Acute leukemias represent the most common malignancy in children worldwide and remain a significant cause of morbidity and mortality, particularly in low- and middle-income countries (LMICs).^1,2 Despite advances in treatment protocols that have markedly improved survival rates in high-income settings, disparities in access to healthcare and treatment adherence continue to challenge outcomes in resource-constrained regions.^3,4 Among the critical barriers identified, geographic accessibility has emerged as a potentially pivotal factor influencing treatment abandonment in pediatric leukemia patients.^5-8 Treatment abandonment, defined as the premature discontinuation or failure to initiate curative therapy, undermines the benefits of modern treatment regimens and is associated with markedly poorer survival outcomes.^9-11

Global evidence underscores the multifaceted role of geographic accessibility in pediatric cancer care. Studies from diverse LMICs, including Honduras, El Salvador, Colombia, Kenya, and India, have documented treatment abandonment rates ranging from 5% to over 50%, frequently correlating with longer travel distances and times to specialized treatment centers.^12-16 For example, in Honduras, travel times exceeding two hours increased the hazard ratio for treatment abandonment and mortality.¹⁵ Similarly, in Kenya and Zambia, distances exceeding 50 kilometers were strongly linked to discontinuation of therapy, compounded by socioeconomic constraints and cultural factors such as parental education, and local beliefs.^12,17 These findings suggest that geographic barriers not only impede access to care but also exacerbate existing socioeconomic vulnerabilities, contributing to treatment non-adherence and survival disparities.

In contrast, studies from high-income countries such as Canada and the Netherlands have found less consistent associations between geographic distance and abandonment, highlighting the influence of broader systemic factors including health insurance coverage, social support, and centralized care coordination (Table 1).^7,18 Nonetheless, disparities persist even within these settings among populations facing socioeconomic disadvantages or residing in remote areas, emphasizing the need to contextualize geographic accessibility within social determinants of health.

Table 1.

Sociodemographic and Clinical Characteristics of the Study Population (n = 574)

Variable	Total n (%)
Child’s sex
Male	316 (55.1)
Female	258 (44.9)
Child´s age at diagnosis
<1 year	9 (1.6)
1 - 4 years	157 (27.4)
5 - 9 years	155 (27)
10 - 14 years	175 (30.5)
15 - 19 years	78 (13.6)
State of residence
Tlaxcala	64 (11.1)
Puebla	326 (56.8)
Oaxaca	184 (32.1)
Year of diagnosis
2021	187 (32.6)
2022	188 (32.8)
2023	199 (34.7)
Public health insurance affiliation
Yes	156 (27.2)
No	418 (72.8)
Leukemia subtype
Acute lymphoblastic leukemia	480 (83.6)
Acute myeloid leukemia	94 (16.4)
Treatment abandonment
No	479 (83.4)
Yes	95 (16.6)

In Mexico, the burden of pediatric acute leukemia is significant, with incidence and mortality rates that reflect both the epidemiological transition and the heterogeneity of healthcare access across regions.^19,20 Prior research conducted in Mexico City and other urban centers has identified socioeconomic factors such as maternal education and social security coverage as predictors of treatment abandonment, but data on geographic accessibility remain limited (Table 1).^21,22 Importantly, Mexico’s south-central region, including the states of Puebla, Oaxaca, and Tlaxcala, faces unique challenges characterized by marked disparities in health infrastructure, high poverty rates, and fragmented service delivery. These states consistently rank among the poorest in the country, with significant portions of the population living in rural or marginalized urban areas where access to specialized pediatric oncology services is constrained by long travel distances, limited transportation options, and economic hardship.

Despite the recognized importance of geographic and socioeconomic barriers, there is a paucity of research focusing on how these factors intersect to influence treatment abandonment among children with acute leukemia in this region. Given the profound impact that treatment discontinuation has on survival, understanding the association between geographic accessibility, socioeconomic determinants and treatment abandonment risk is critical to design interventions that improve care retention and outcomes. Moreover, such knowledge is essential to inform regional health policy and resource allocation in a context where structural inequities contribute to persistent cancer disparities.

The aim of the present study was to evaluate the association between geographic accessibility, socioeconomic conditions, and the risk of treatment abandonment among pediatric patients with acute leukemia in the south-central region of Mexico.

Methods

A multicenter prospective cohort study was conducted to include incident, newly diagnosed cases of acute leukemia among children residing in the Mexican states of Puebla, Tlaxcala, and Oaxaca. Since January 2021, our research group has coordinated the first population-based pediatric leukemia registry in this region, which systematically identifies every new diagnosis through active case ascertainment and the participation of all public and private hospitals providing pediatric oncology care.²³ All eligible patients were consecutively included at the time of diagnosis. For the present analysis, children treated exclusively in private hospitals were excluded; although these institutions contribute to the registry, treatment abandonment is exceedingly rare in that setting due to distinct socioeconomic profiles and care pathways and they account for only ∼1.5% of new diagnoses.

The study was conducted in full accordance with the ethical principles of the Declaration of Helsinki of 1975, as revised in 2024. Ethical approval was received from the National Committee for Scientific Research and Ethics on February 27, 2020, under registration number R-2020-785-022. Written informed consent was obtained from the parents or legal guardians of all participating children prior to enrollment, and assent was requested from patients aged 8 years and older in accordance with national regulations and international guidelines for research involving minors. All patient details have been de-identified. The reporting of this study conforms to the STROBE guidelines.²⁴

Treatment Protocols

Across all participating hospitals, first-line therapy for acute lymphoblastic leukemia (ALL) follows a modified St. Jude Total XV protocol. Acute myeloid leukemia (AML) is treated with BFM-93 or NOPHO-derived regimens, while acute promyelocytic leukemia (AML-M3) is managed according to PETHEMA protocols. Induction therapy for ALL typically requires approximately four weeks of continuous hospitalization, followed by shorter, planned admissions during consolidation and intensification phases. Consolidation includes four brief hospitalizations of approximately four days each for high-dose methotrexate administration. In high-risk patients, a seven-day hospitalization may be required for re-intensification. The subsequent 30-month continuation phase is delivered almost entirely on an outpatient basis, and requires frequent travel to the treating institution, although one additional four-day inpatient stay is typically required on week 19. AML regimens involve multi-week inpatient blocks during induction and consolidation, with only brief recovery intervals at home between cycles.

Family Accommodation and Support During Treatment

Families often use temporary housing near pediatric oncology centers when treatment facilities are far from home. Each participating state has at least one hospital-affiliated shelter that provides accommodation for a caregiver and the patient during intensive treatment periods. When shelter capacity is limited, families often rent nearby rooms at their own expense. In Puebla, additional support is available through the nonprofit organization Una Nueva Esperanza A.B.P., which offers lodging and transportation assistance to children treated at the Hospital para el Niño Poblano, including some patients from Tlaxcala.²⁵ These arrangements facilitate continuity of care during prolonged hospitalizations and during outpatient phases requiring frequent visits to the treating center.

The primary outcome was treatment abandonment, defined according to the International Society of Pediatric Oncology (SIOP) as the failure to initiate treatment or discontinuation of treatment for four or more consecutive weeks.²⁶ This outcome was assessed as a dichotomous variable (yes or no) throughout the follow-up period, from the date of diagnosis up to the end of follow-up. All participants were followed for at least one year after diagnosis, with follow-up continuing through July 30, 2025. Information on treatment status and continuity of care was gathered through quarterly reviews of medical records, direct communication with treating physicians, and, when necessary, phone interviews with caregivers.

Study Variables

The present study analyzed a range of variables grouped into four main categories: demographic characteristics, clinical characteristics, geographic accessibility, and socioeconomic factors.

Demographic variables: Child’s sex (female or male) and age at diagnosis, categorized into five groups: <1 year, 1-4 years, 5-9 years, 10-14 years, and 15-19 years.

Clinical characteristics: Leukemia subtype (acute lymphoblastic leukemia or acute myeloid leukemia) and year of diagnosis (2021, 2022, or 2023).

Geographic accessibility: Geographic accessibility was assessed using three complementary measures: (1) network-based travel distance (km), (2) estimated travel time (hours), and (3) area of residence (urban vs rural). Coordinates of each child’s residence were geocoded and input into OpenRouteService Tools (ORS Tools). Distances and travel times were calculated using within Quantum GIS (QGIS).²⁷ ORS Tools interfaces with the OpenRouteService API, which performs routing over the existing road network using OpenStreetMap (OSM) data, ensuring that distance and time estimates reflect network-based travel rather than Euclidean (straight-line) measures. Additionally, to evaluate the influence of terrain on travel times, we performed a supplementary recalculation using slope-adjusted speeds derived from the digital elevation model (DEM), which incorporates road-segment slope to modify travel speed without altering the underlying route geometry.

Cut-Off Point Determination

Distance and travel time variables showed non-parametric distributions. Optimal thresholds were determined using receiver operating characteristic (ROC) curve analysis, with treatment abandonment (yes/no) as the outcome variable. The Youden Index (J = sensitivity + specificity – 1) was used to identify the cut-off values that maximized combined sensitivity and specificity. The resulting thresholds were 141 km for distance and 1.6 hours for travel time. Figure 1 illustrates the geographic distribution of childhood leukemia cases across the participating states, highlighting differences in distance from treatment centers and urban vs rural residence.

Figure 1.

Geographic distribution of childhood leukemia cases by distance to treatment centers and area of residence in Puebla, Oaxaca, and Tlaxcala (2021-2023).

Area of Residence Classification

To classify each residence as urban or rural, we applied a spatial methodology using official data from the National Institute of Statistics and Geography (INEGI) corresponding to the Basic Geo-Statistical Areas (AGEB). AGEBs are territorial units defined by INEGI for the collection and analysis of statistical information, and their classification as urban or rural is based on criteria such as population size, density, and the presence of urban infrastructure. Official geospatial files of the AGEBs were downloaded from the INEGI website in shapefile or GeoJSON formats, which include the geometries and attributes necessary for classification. The geographic coordinates of each residence were geocoded as spatial points and a spatial join was performed with the AGEB polygons to assign the corresponding area attributes. Based on these attributes and INEGI’s official criteria, each residence was classified as urban or rural.²⁸

Socioeconomic and Demographic Variables

Socioeconomic factors included public health insurance affiliation (yes/no), parental education levels (≥9 or <9 years). Additional variables included the number of siblings (<3 or ≥3) and parental occupation. Parental occupation was classified into four categories: (1) formal/professional employment, (2) service/informal sector, (3) agricultural/manual labor, and (4) unknown/unemployed. This classification was developed based on the following rationale: formal/professional occupations typically offer stable employment, regular income, and access to social security; service/informal sector jobs were grouped separately due to their irregular earnings and lack of formal labor protections; and agricultural/manual labor was treated as its own category given its high socioeconomic vulnerability, characterized by seasonal, low-wage, and unstable working conditions that may hinder a family’s ability to sustain treatment. Operational definitions and representative job examples for each category are detailed in Supplemental Table 1.

Statistical Analyses

Descriptive statistics were used to summarize the distribution of demographic, clinical, geographic, and socioeconomic characteristics of children with leukemia. Categorical variables were reported as frequencies and percentages, while continuous variables were summarized using medians and ranges.

Associations between treatment abandonment and individual covariates were initially explored using univariate analysis. For categorical variables, comparisons were conducted using the chi-square test or Fisher’s exact test as appropriate, and crude odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. For continuous variables, such as distance and travel time to the hospital, comparisons were performed using the Mann–Whitney U test, and ORs were estimated using univariate logistic regression.

Variables with a P-value <0.10 in the univariate analysis, as well as variables considered clinically relevant based on previous literature (eg, child’s sex and age, year of diagnosis, leukemia subtype), were included in the multivariate model.

Distance from the child’s residence to the hospital and travel time were highly correlated (Spearman’s rho = 0.85, P < 0.001). To avoid multicollinearity in the multivariable model, only distance to the hospital was included.

Multivariable logistic regression was then performed to estimate adjusted odds ratios (aORs) and 95% CIs for factors independently associated with treatment abandonment. Adjusted analyses accounted for potential confounders, including demographic and clinical variables.

All statistical analyses were conducted using IBM SPSS Statistics for Windows, Version 27.0 (IBM Corp., Armonk, NY, USA). A two-tailed P-value <0.05 was considered statistically significant.

Results

A total of 574 children diagnosed with acute leukemia between 2021 and 2023 were included in the analysis. The majority were male (55.1%) and aged between 1 and 14 years at the time of diagnosis (85% overall). Most patients resided in the state of Puebla (56.8%), followed by Oaxaca (32.1%) and Tlaxcala (11.1%). Acute lymphoblastic leukemia (ALL) was the predominant subtype (83.6%), and most patients were diagnosed in either 2022 (32.8%) or 2023 (34.7%). Notably, 72.8% of the cohort lacked public health insurance, and treatment abandonment occurred in 16.6% of patients (n = 95) (Table 1). Among the 95 children who discontinued therapy, the most frequent category was parental or family decision to stop treatment (43.1%, n = 41). In these cases, no specific underlying reason was documented, and the precise motivation for discontinuation is unknown. Clinical deterioration, including relapse, transition to palliative care, or severe complications, accounted for 24.2% (n = 23). Socioeconomic or structural barriers, such as financial hardship, transportation difficulties, or loss of insurance, were reported in 17.9% (n = 17). Beliefs, religion, mistrust, or pursuit of alternative or non-medical therapies were noted in 7.4% (n = 7), and treatment-related toxicity in 7.4% (n = 7).

Table 2 shows the univariate associations between treatment abandonment and sociodemographic, clinical, and geographic variables. No significant differences were observed by sex, age at diagnosis, or leukemia subtype. Likewise, year of diagnosis did not reach statistical significance, although there was a trend toward a lower risk of abandonment in 2022 compared with 2021 (OR = 0.60, 95% CI: 0.34-1.04, P = 0.07).

Table 2.

Association Between Treatment Abandonment and Sociodemographic, Clinical, and Geographic Variables in Children With Leukemia

Variable	No n (%)/Median (min-max)	Yes n (%)/Median (min-max)	OR (95% CI)	P-value*
Variable	Treatment abandonment		OR (95% CI)	P-value*
Demographic characteristics
Child´s sex
Female	215 (44.9)	43 (45.3)	-------
Male	264 (55.1)	52 (54.7)	0.98 (0.63-1.53)	0.94
Child´s age at diagnosis
<1 year	7 (1.5)	2 (2.1)	------
1 - 4 years	130 (27.1)	27 (28.4)	0.72 (0.14-3.69)	0.71
5 - 9 years	135 (28.2)	20 (21.1)	0.51 (0.10-2.67)	0.43
10 - 14 years	149 (31.1)	26 (27.4)	0.61 (0.12-3.10)	0.55
15 - 19 years	58 (12.1)	20 (21.1)	1.20 (0.23-6.29)	0.82
Clinical characteristics
Leukemia subtype
Acute lymphoblastic leukemia	403 (84.1)	77 (81.1)	------
Acute myeloid leukemia	76 (15.9)	18 (18.9)	1.24 (0.70-2.18)	0.45
Year of diagnosis
2021	149 (31.1)	38 (40)	------
2022	163 (34)	25 (26.3)	0.60 (0.34-1.04)	0.07
2023	167 (34.9)	32 (33.7)	0.75 (0.44-1.26)	0.28
Geographic factors
Distance from child’s residence to hospital (km)	57.28 (1.73-679.80)	93.08 (4.05-336.68)	1.00 (0.99-1.01)	0.24
Distance categories
<141 km	355 (74.1)	58 (61.1)	------
≥141 km	124 (25.9)	37 (38.9)	1.82 (1.15-2.89)	0.01
Travel time from child´s residence to hospital (hours)	0.89 (0.07-7.67)	1.20 (0.11-4.87)	1.11 (0.95-1.30)	0.17
Travel time categories
<1.6 hours	614 (65.6)	53 (55.8)	------
≥1.6 hours	165 (34.4)	42 (44.2)	1.50 (0.96-2.35)	0.07
Area of residence
Urban	396 (82.7)	75 (78.9)	------
Rural	83 (17.3)	20 (21.1)	1.27 (0.73-2.19)	0.38
Socioeconomic factors
Public health insurance affiliation
Yes	144 (30.1)	12 (12.6)	-------
No	335 (69.9)	83 (87.4)	2.97 (1.57-5.61)	<0.001
Mother´s age at diagnosis
<40 years	363 (75.8)	70 (73.7)	-------
≥40 years	116 (24.2)	25 (26.3)	1.11 (0.67-1.84)	0.66
Maternal Education
≥9 years	160 (33.4)	21 (22.1)	------
<9 years	319 (66.6)	74 (77.9)	1.76 (1.05-2.97)	0.03
Paternal education
≥9 years	133 (27.8)	17 (17.9)	------
<9 years	346 (72.2)	78 (82.1)	1.76 (1.01-3.09)	0.04
Number of siblings
<3	236 (49.3)	36 (37.9)	------
≥3	243 (50.7)	59 (62.1)	1.59 (1.01-2.50)	0.04
Father’s occupation
Formal/professional employment	100 (20.9)	12 (12.6)	------
Service/informal sector	68 (14.2)	6 (6.3)	0.73 (0.26-2.05)	0.55
Agricultural/manual labor	227 (47.4)	61 (64.2)	2.23 (1.15-4.34)	0.01
Unknown/no data/unemployed	84 (17.5)	16 (16.8)	1.58 (0.71-3.54)	0.25
Mother’s occupation
Formal/professional employment	68 (14.2)	11 (11.6)	------
Service/informal sector	52 (10.9)	10 (10.5)	1.18 (0.46-3.01)	0.71
Agricultural/manual labor	322 (67.2)	69 (72.6)	1.32 (0.66-2.63)	0.42
Unknown/no data/unemployed/homemaker	37 (7.7)	5 (5.3)	0.83 (0.27-2.58)	0.75

OR: Odds ratio; 95% CI: 95% confidence interval; *Chi-square test.

Geographic accessibility indicators revealed important differences. Children living ≥141 km from the treatment center had a significantly higher risk of abandonment compared with those residing closer (OR = 1.82, 95% CI: 1.15-2.89, P = 0.01). Travel time ≥1.6 hours also showed a trend toward higher risk, though not statistically significant (OR = 1.50, 95% CI: 0.96-2.35, P = 0.07). Residence in rural vs urban areas was not associated with abandonment.

Among socioeconomic factors, lack of public health insurance emerged as the strongest predictor of abandonment (OR = 2.97, 95% CI: 1.57-5.61, P < 0.001). Additional risk factors included low maternal education (<9 years) (OR = 1.76, 95% CI: 1.05-2.97, P = 0.03), low paternal education (<9 years) (OR = 1.76, 95% CI: 1.01-3.09, P = 0.04), having three or more siblings (OR = 1.59, 95% CI: 1.01-2.50, P = 0.04), and paternal employment in agricultural or manual labor (OR = 2.23, 95% CI: 1.15-4.34, P = 0.01). No significant associations were observed for parental age or maternal occupation.

Multivariate Analysis

To identify independent predictors of treatment abandonment, we constructed a multivariable logistic regression model including variables with P < 0.10 in univariate analysis, as well as others identified as relevant in the literature (Table 3). After adjustment, two factors remained significantly associated with higher odds of abandonment: lack of public health insurance (aOR = 2.83, 95% CI: 1.39-5.76, P < 0.01) and living ≥141 km from the treatment center (aOR = 1.68, 95% CI: 1.02-2.74, P = 0.03).

Table 3.

Multivariable Logistic Regression Analysis

Variable	aOR (95% CI)	P-value
Public health insurance affiliation (no)	2.83 (1.39-5.76)	<0.01
Distance to hospital (≥141 km)	1.68 (1.02-2.74)	0.03
Number of siblings (≥3)	1.40 (0.86-2.27)	0.17
Maternal education (<9 years)	1.16 (0.65-2.06)	0.61
Fathers’ occupation (agricultural/manual labor)	1.17 (0.54-2.53)	0.68

aOR: adjusted odds ratio; 95% CI: 95% confidence interval.

Adjusted by child’s sex and age, year of diagnosis, and leukemia subtype.

Other factors that were significant in univariate analysis lost statistical significance in the adjusted model, although they continued to show elevated risks. These included having three or more siblings (aOR = 1.40, 95% CI: 0.86-2.27, P = 0.17), maternal education <9 years (aOR = 1.16, 95% CI: 0.65-2.06, P = 0.61), and paternal occupation in agricultural or manual labor (aOR = 1.17, 95% CI: 0.54-2.53, P = 0.68).

In a separate multivariable model, we substituted travel distance with travel time to the hospital. After adjusting for the same covariates, no significant association was observed between travel time and treatment abandonment (aOR = 1.25; 95% CI: 0.77-2.02; P = 0.35).

Stratified analysis

To further explore the interaction between health insurance status and geographic barriers, we conducted a stratified analysis of treatment abandonment by public insurance affiliation and distance from the hospital. Among patients living <141 km from the treatment center, abandonment was significantly more frequent among those without public health insurance (n = 51; 17.7%) compared with insured patients (n = 7; 5.6%) (P = 0.001). In contrast, at ≥141 km, abandonment rates were higher overall, but the difference between uninsured (n = 32; 24.6%) and insured patients (n = 5; 16.1%) was not statistically significant (P = 0.31). The Mantel–Haenszel common odds ratio confirmed that lack of public health insurance was associated with nearly a threefold higher risk of abandonment across strata (OR = 2.80, 95% CI: 1.48-5.29, P = 0.002).

Discussion

In this study of 574 children diagnosed with acute leukemia between 2021 and 2023 across south-central Mexico, two factors emerged as independent predictors of treatment abandonment: lack of public health insurance and residing ≥141 km from the treatment center. Lack of insurance was associated with nearly a threefold increase in abandonment, while living farther than 141 km independently raised the odds by 68%. Together, these findings underscore the combined influence of financial protection and geographic accessibility on treatment adherence.

Consistent with this interpretation, the protective effect of public health insurance is in line with prior national evidence demonstrating that financial coverage expands access and improves outcomes among uninsured pediatric patients, even though geographic and socioeconomic disparities persist.^29,30 Stratified analyses provided important insight into this relationship. Among families living <141 km from treatment centers, uninsured children were more than three times more likely to abandon treatment than insured peers, a finding that underscores the dominant influence of financial vulnerability even in settings where geographic barriers are relatively limited. At ≥141 km, abandonment was uniformly high and differences by insurance status were no longer statistically significant, likely reflecting the accumulation of additional challenges in remote areas, including deeper socioeconomic marginalization and limited transportation options. Consequently, within south-central Mexico, financial protection remains the central determinant of treatment continuity, with geographic barriers further compounding existing vulnerabilities.

Geographic accessibility has shown inconsistent associations with abandonment across pediatric oncology settings globally. Our finding that distance, but not travel time, was an independent predictor aligns with studies from Guatemala, where longer distances were linked to higher abandonment and poorer survival.³¹ Conversely, cohorts in India^32,33 and Canada^34,35 have reported no association between distance and adherence, while evidence from Kenya and the United States suggests that longer travel distances may adversely affect outcomes, though with varying effect sizes.^36,37 These differences underscore that the impact of distance is context-dependent, shaped by health system capacity, transportation infrastructure, and available social support.^8,38

Beyond insurance status and distance, other structural vulnerabilities contributed to abandonment. Larger family size and paternal employment in agricultural/manual labor, occupations marked by income instability and limited flexibility, were associated with higher discontinuation, echoing findings from El Salvador, Colombia, and Pakistan.^39,40 Although paternal agricultural/manual labor did not retain significance in multivariable models, its socioeconomic characteristics likely influence a family’s ability to sustain long-term treatment engagement.

In contrast, individual demographic characteristics, including age, sex, maternal education, and maternal age, showed no association with abandonment, consistent with studies from Central America and Southeast Asia. These findings align with global evidence indicating that structural socioeconomic deprivation, rather than patient-level demographics, drives most episodes of abandonment in low- and middle-income countries.

While these broader social vulnerabilities appear influential, individual demographic characteristics may play a more limited role. In our cohort, patient age and sex were not associated with abandonment, a finding consistent with studies from El Salvador, and Indonesia, where such variables also showed minimal predictive value.^39,41 Similarly, maternal education below the secondary level, though highlighted as a risk factor in some settings,^42,43 did not emerge as significant predictor of treatment abandonment in this analysis. Maternal age, whether under or over 39 years, was likewise not associated with increased risk.⁴²

The reasons documented for abandonment were heterogeneous and often nonspecific. Nearly half of families (43.1%) discontinued therapy without a defined explanation, consistent with reports from other LMICs where proximal reasons such as “parental decision” or “voluntary discharge” do not fully capture the underlying economic and logistical constraints influencing discontinuation. Only a small proportion of events were attributed to cultural beliefs, alternative medicine, or treatment toxicity, further supporting that structural and socioeconomic pressures, rather than clinical or cultural factors, are the predominant forces leading to discontinuation. Patterns across treatment phases reinforce this interpretation: most abandonment occurred during outpatient-dependent stages, including 80.5% during maintenance for ALL and 72.2% during post-induction therapy for AML, underscoring the cumulative burden placed on families during prolonged periods of frequent travel and caregiving. Disease subtype, including ALL and AML, and clinical risk group (infant leukemia) showed no association with treatment abandonment in either univariate or multivariate analyses, emphasizing that discontinuation in this setting reflects systemic rather than biological determinants.

Study Limitations

A potential limitation of this study is the possibility that not all children diagnosed with leukemia in the south-central region of Mexico were included. Importantly, this potential source of selection bias is minimized by the presence of an established population-based childhood leukemia registry in the south-central region of Mexico, to which all participating hospitals contribute prospectively. Since January 2021, these institutions have systematically reported every newly diagnosed case to the registry, reinforcing the completeness and representativeness of the study sample.

Geographic accessibility estimates were derived using a validated road-network routing model, an important methodological strength given the substantial topographic and infrastructural heterogeneity across the three participating states. This approach incorporates actual road geometry and surface connectivity, offering a far more realistic representation of travel burden than linear (Euclidean) distance or administrative approximations. However, a key limitation is the absence of formal public transportation data, such as fixed routes, schedules, or service frequency, which are not systematically recorded in Oaxaca, Puebla, or Tlaxcala. In many rural communities, transportation systems are informal, irregular, or altogether absent, meaning that families often depend on private vehicles, shared taxis, or community-based transport.

To address concerns regarding terrain-related effects on travel-time estimation, particularly in mountainous areas, we conducted a supplementary sensitivity analysis using slope-adjusted speeds derived from a DEM. Because these adjustments affect travel speed but not road geometry, distances remained unchanged. The DEM-adjusted travel-time estimates were highly correlated with those generated by the primary routing model (Pearson r = 0.994, P < 0.001), indicating that topography did not materially alter accessibility estimates. Nonetheless, unmeasured factors such as transport availability, travel costs, multimodal travel segments, and waiting times in rural transport hubs could not be captured, and may have contributed to an underestimation of the true travel burden for some families. Under these conditions, network-based distance and road-network travel time represent the most feasible and reproducible metrics currently available for quantifying geographic access to pediatric oncology services in this region.

No formal a priori sample size calculation was performed, as the study included all incident cases identified during the study period. While the final sample of patients provided adequate power for the primary analyses, some subgroup comparisons may remain underpowered, and this should be interpreted as a limitation.

A further limitation involves the assessment of socioeconomic status. Family income could not be analyzed because 58% of parents declined to report their earnings at the time of the interview, often due to safety concerns or mistrust and the remaining reports could not be independently verified. Consistent with ethical principles of confidentiality and data integrity, income was therefore excluded. Nevertheless, we used well-established socioeconomic proxies, public health insurance affiliation, parental education, and paternal occupation, which correlate strongly with socioeconomic position and showed consistent associations with treatment abandonment.^42,44

Implications for Policy and Practice

The protective role of public health insurance underscores critical policy levers to reduce treatment abandonment. Strengthening and expanding financial protection, through sustainable insurance coverage and reduced indirect treatment costs, may substantially improve treatment completion.³⁰ In this context, OncoCREAN provides a model of comprehensive support for insured children under the Mexican Social Security Institute (IMSS). Beyond bringing specialized pediatric oncology services closer to patients’ home states, OncoCREAN ensures full coverage of medical care, transportation stipends, and lodging assistance for families, thereby addressing both clinical needs and the indirect costs that often drive discontinuation.⁴⁵

National efforts such as the PRONAII Childhood Leukemia Strategy and the establishment of the Childhood Cancer Cytomics Laboratory have further strengthened diagnostic capacity and risk stratification for children with and without public insurance, helping to reduce delays and improve prognostic accuracy.^46,47 Additionally, the recently launched government’s financial support program for families of pediatric cancer patients without public health insurance represents a meaningful step toward mitigating economic barriers to care.⁴⁸

Complementing these public-sector initiatives, nonprofit organizations such as Una Nueva Esperanza A.B.P., based in Puebla, have demonstrated notable effectiveness in reducing treatment abandonment among children without public health insurance affiliation. This organization provides comprehensive support, including lodging, transportation, and coordinated psychological, educational, dental, oncological, and nutritional services to families from Puebla and Tlaxcala.²⁵ During the study period, children who received assistance from Una Nueva Esperanza (n = 124) had an abandonment rate of 9.6% (n = 12), compared with 24.1% (n = 71) among those who did not receive support (n = 294) (P < 0.001), underscoring the substantial impact of these interventions in improving adherence and mitigating socioeconomic and geographic barriers to care.

Finally, expanding social and educational support for families, particularly those with lower maternal education or high caregiving burdens, can further reinforce adherence and help ensure children complete the full course of therapy.⁸ Collectively, these strategies illustrate that reducing treatment abandonment is not solely a clinical challenge but also a matter of social equity and justice, particularly in regions where socioeconomic vulnerability remains high.

Conclusions

Treatment abandonment remains one of the most pressing barriers to improving survival outcomes for children with leukemia in low- and middle-income countries. In this study, lack of public health insurance emerged as the most consistent and significant predictor of abandonment, even after adjusting for geographic and socioeconomic variables. These findings reflect the broader realities faced by populations marked by structural vulnerability, such as those in the south-central region of Mexico where this study was conducted, where longstanding socioeconomic inequalities and fragmented access to specialized care continue to shape disparities in cancer outcomes.

Reducing treatment abandonment in pediatric oncology will require interventions beyond the clinical domain. Expanding access to comprehensive public health insurance, decentralizing specialized cancer care, and providing targeted social support for vulnerable families are critical strategies to improve adherence and equity. Policy reforms must take into account the full context in which patients live, recognizing that the capacity to complete treatment is shaped as much by social and economic conditions as by medical need. Without structural change, even the most effective therapeutic protocols will continue to fall short for the children who are unable to access or sustain care.

Supplemental Material

Supplemental Material - Geographic and Socioeconomic Determinants of Treatment Abandonment in Pediatric Acute Leukemia: A Cohort Study in South-Central Mexico

Supplemental Material for Geographic and Socioeconomic Determinants of Treatment Abandonment in Pediatric Acute Leukemia: A Cohort Study in South-Central Mexico by Juan Carlos Núñez-Enriquez, Nuria Citlali Luna-Silva, Karen Jacuinde-Trejo, Janet Flores-Lujano, Daniela Medina-León, Erika Alarcón-Ruiz, Miguel Ángel Garrido-Hernández, Cynthia Shanat Cruz-Medina, Diana Tinoco-Montejano, Ma del Rocío Baños-Lara, María de los Ángeles Del Campo-Martínez, David Aldebarán Duarte-Rodríguez, Aldo Allende-López, Diana Casique-Aguirre, Jesús Elizarrarás-Rivas, Daniela Olvera-Caraza, Juan Carlos Solís-Poblano, Vanesa Terán-Cerqueda, Dalia Ramírez-Ramírez, Andrea Huerta-Moreno, Pierre Mitchel Aristil-Chery, Rubí Romo-Rodríguez, Enoch Alvarez-Rodríguez, Lourdes Esthela Juan Lien-Chang, Gabriela Zamora-Herrera, Brianda García Hidalgo, Wilfrido Herrera-Olivares, Guillermo José Ruíz-Arguelles, Moisés Manuel Gallardo-Pérez, Lénica Anahí Chávez-Aguilar, Aquilino Márquez-Toledo, Lena Sarahí Cano-Cuapio, Raquel Hernández Ramos, María Angélica Martínez-Martell², Anabel Beatriz Ramirez-Ramirez, Nalyn Rodriguez Brindis, Alvaro José Montiel-Jarquín, César Alejandro Galván-Díaz, Liliana Velasco-Hidalgo, Aurora Medina-Sanson, María de Lourdes Gutiérrez-Rivera, Alan Cárdenas-Conejo, Adrián Morales-Maravilla, Nora Patricia Victorio-García, Vilma Carolina Bekker-Méndez, María de los Ángeles Romero-Tlalolini, Juan Carlos Rodríguez-Espinosa, Minerva Mata-Rocha, Amanda Idaric Olivares-Sosa, Haydeé Rosas-Vargas, Silvia Jiménez-Morales, Martha Eugenia Juárez Martínez, Mariana Cárdenas-González, Juan Manuel Mejía-Aranguré, Enrique López-Aguilar, Marta Zapata-Tarrés, Rosana Pelayo in Cancer Control.

Footnotes

Acknowledgments

Invaluable contributions and dedication of the PRONAII Childhood Leukemia fieldwork team. Each member plays a vital role in advancing a project. We are grateful to Fundacion IMSS, A.C. for exceptional administrative management of the Leukemia National Project.

ORCID iDs

Juan Carlos Núñez-Enriquez

Dalia Ramírez-Ramírez

Rubí Romo-Rodríguez

Gabriela Zamora-Herrera

Lénica Anahí Chávez-Aguilar

Ethical Considerations

Ethical approval was received from the National Committee for Scientific Research and Ethics on February 27, 2020, under registration number R-2020-785-022. Written informed consent was obtained from the parents or legal guardians of all participating children prior to enrollment, and assent was requested from patients aged 8 years and older in accordance with national regulations and international guidelines for research involving minors.

Author Contributions

Núñez-Enriquez JC, together with Rosana Pelayo, contributed to the conceptualization, resource provision, project administration, funding acquisition, overall supervision of the project, original draft preparation, and critical revision of the manuscript. Luna-Silva N.C., Jacuinde-Trejo K., Flores-Lujano J., Medina-León D., Alarcón-Ruiz E., Garrido-Hernández M.A., Shanat Cruz-Medina C., Tinoco-Montejano D., Baños-Lara M.R., Del Campo-Martínez M.A., Duarte-Rodríguez D.A., Allende-López A., Casique-Aguirre D., Elizarrarás-Rivas J., Olvera-Caraza D., Solís-Poblano J.C., Terán-Cerqueda V., Ramírez-Ramírez D., Huerta-Moreno A., Aristil-Chery P.M., Romo-Rodríguez R., Alvarez-Rodríguez E., Juan Lien-Chang L.E., Zamora-Herrera G., García Hidalgo B., Herrera-Olivares W., Ruíz-Arguelles G.J., Gallardo-Pérez M.M., Chávez-Aguilar L.A., Márquez-Toledo A., Cano-Cuapio L.S., Hernández Ramos R., Martínez-Martell M.A., Ramirez-Ramirez A.B., Rodriguez Brindis N., Montiel-Jarquín A.J., Galván-Díaz C.A., Velasco-Hidalgo L., Medina-Sanson A., Gutiérrez-Rivera M.L., Cárdenas-Conejo A., Morales-Maravilla A., Victorio-García N.P., Bekker-Méndez V.C., Romero-Tlalolini M.A., Rodríguez-Espinosa J.C., Mata-Rocha M., Olivares-Sosa A.I., Rosas-Vargas H., Jiménez-Morales S., Juárez Martínez M.E., Cárdenas-González M., Mejía-Aranguré J.M., López-Aguilar E., Zapata-Tarrés M, participated in the investigation, data curation, formal analysis, original draft writing. contributed to results validation, methodological analysis, data visualization, and manuscript review. All authors reviewed and approved the final version of the manuscript.

Funding

The authors declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by grants from the Secretaría de Ciencia, Humanidades, Tecnología e Innovación (SECIHTI), previously known as the Consejo Nacional de Humanidades, Ciencias y Tecnologías (CONAHCYT) FORDECYT-PRONACES 302994 to RP, FORDECYT-PRONACES 303083 to MRB-L and FORDECYT-PRONACES 303019 to JN-E.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

All raw data are available to share upon request.*

Use of Artificial Intelligence in Manuscript Preparation

No artificial intelligence tools were used in the conception, design, data collection, analysis, or writing of this manuscript. All work was conducted and written solely by the authors.

Supplemental Material

Supplemental material for this article is available online.

Appendix

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