Abstract
Background
Globally, breast cancer is one of the most common cancers among women, especially in low- and middle-income countries (LMICs). The health insurance status in LMICs is approximately 31.1% as of 2023, indicating that out-of-pocket (OOP) payments are common in these countries. In Tanzania, women experience financial difficulties related to breast cancer, which can put both the women and their households at risk of catastrophic health expenditures. The co-payments of health insurance can improve access to health services. This study aimed to assess factors associated with breast cancer screening (BCS) among insured and uninsured women in Tanzania.
Methods and tools
This study conducted a cross-sectional secondary study using the 2022 Tanzanian Demographic Health Survey (TDHS) 2022 by employing a quantitative approach. The study included a total of 15,254 women from a survey conducted by the Tanzania National Bureau of Statistics (NBS). During the analysis, data were weighted using individual women's sample weights to account for the complex sampling design and non-response rate. The univariate, bivariate and multivariable logistic regression analysis models were used to determine the association between independent variables (health insurance and other socio-economic factors) and dependent variables (BCS status). All analyses were performed using Stata version 16, and svyset commands were applied in the multivariable logistic regression analysis to account for the complex survey design.
Results
Among the total respondents, 15,254 were included in the study, a total of 15,188 women were asked if they were screened for breast cancer. Only 4.95% were screened for breast cancer. Health insurance coverage had a statistically significant association with BCS for both the crude (COR 4.39; p-value < 0.001) and adjusted model (OR 2.17; p-value < 0.001). This means that the insured women had four times higher odds of BCS than the uninsured women for the crude model and two times for the adjusted model. In addition, age, education level, current working status and those who visited the health facility at least 12 months were significantly associated with the BCS for both crude and adjusted models.
Conclusion
This study emphasizes the significance of factors associated with BCS in Tanzania. These factors include being an active member of a health insurance scheme, socioeconomic status, and education. Therefore, policymakers, especially the Ministry of Health and the President’s Office-Regional Administration and Local Government, should integrate these factors into national strategies and guidelines to improve equitable access to BCS. Targeted interventions that address financial and socio-economic barriers will be essential for increasing screening coverage and reducing the burden of breast cancer among women in Tanzania.
Keywords: Breast cancer, Breast cancer screening, Health insurance, Out-of-pocket payments, Tanzania, DHS
Introduction
Breast cancer is among the leading cancers, whereby in 2022 it was the second leading global cancer, which comprised 11.6% of all new cancer cases [1]. In addition, this cancer is the fourth leading cause of cancer mortality, 6.9% of all cancer deaths worldwide [2]. In most low-resource settings, there has been an emerging burden of cancer cases including breast cancer, which might probably be partly a result of ongoing poverty [3] Breast cancer is among the common cancers in Tanzania which kill and impair more women than most illnesses in the country. It is among the leading forms of cancer for both morbidity and mortality among women in Tanzania [4].
The economic losses resulting from this burden of disease are estimated to increase up to US$7 trillion [5, 6]. However, it has been suggested by the World Health Organization (WHO) that strategic public health interventions, including organized screening programs, early-diagnosis programs, and primary health care integration, may greatly reduce the breast cancer burden by 2% of this estimated economic loss on a global scale [7]. Nevertheless, the combined costs of interventions, treatment, and premature deaths from breast cancer continue to impose a growing burden on women and households in LMICs [3, 8].
Breast cancer presents with diverse short- and long-term symptoms, many of which limit women’s ability to engage in productive activities. At the same time, the same person is expected to attend long-term care, in which resources for medical and non-medical costs are highly needed. Most of the individuals and households spending their resources in seeking services and management of breast cancer in low-income settings have the possibility of exhausting their resources. Thus, individuals and households are subjected to long-term costs related to visits to health facilities and overall health care which might push them into catastrophic health expenditure [9, 10].
Early diagnosis can provide proper treatment and subsequently reduce mortality caused by this disease at large. Setting priorities on access to routine screening and appropriate treatment services at the health facility can ensure the early detection of cases before reaching severe stages of disease, which may reduce the burden of this cancer [11].
Among women of Tanzania, early detection campaigns and programs have been among the top priorities in health care as an attempt to slow down the effects of this disease. The Tanzanian National Guidelines for Early Diagnosis of Breast Cancer and Referral for Treatment place a strong emphasis on early diagnosis. This process is presented in three stages: awareness of cancer symptoms and access to care; clinical evaluation, diagnosis, and staging; and access to treatment [12]. Addressing and reducing the impact of breast cancer has been a priority not only for the government but also for Civil Society Organisations (CSOs)including medical associations across different cadres. Such efforts are hampered by a low number of skilled health workers with the ability to diagnose breast cancer at early stages, the inability of some women to access health services and the inadequacy of other resources in primary health services [13]. The prevailing challenges in the health service have negatively influenced the pathway of breast cancer patients in early detection, diagnosis, and treatment in most geographical areas. It is estimated that 80% of women suffering from breast cancer present themselves in appropriate health facilities at advanced stages of the disease [14]. Previous evidence presents low uptake of breast cancer screening, early detection, diagnosis and treatment as common barriers towards fighting against this disease [15]. This might be due to poor socio-economic status, health insurance status and education among women. Most studies have focused on awareness and mortality of these cancers [4, 16, 17], but few studies have examined the relationship between health insurance and breast cancer screening at health facilities. Health insurance is believed to be a bridge to access health services and that improve equity in access to health services among the population, including the disadvantaged groups like women.
The majority of women and households in LMICs are neither shielded by health insurance nor other prepayment health financing mechanisms for funding their health expenditure, especially in the event of breast cancer. The households which were recognized as insured by any kind of insurance in LMICs stood at 31.1% in 2023 [18, 19]. In Tanzania, health insurance coverage as a percentage of the total population was 14% in 2020 and increased to 15.3% in both 2022 and 2023. Despite this slight increase, national health insurance coverage remains well below the targeted threshold of 58% population coverage by 2025/2026, which is a key step toward achieving Universal Health Coverage [20]. This implies that out-of-pocket (OOP) payments are common in fully funding health services, and sometimes as a co-payment to health insurance to access some services, depending on the insurance policies [10]. Such ongoing OOP expenses related to seeking and utilization of health services related to breast cancer by individuals and households have a high potential of incurring high costs and subsequently leading to catastrophic health expenditures [19, 21]. This study aimed to assess factors associated with breast cancer screening among insured and uninsured women in Tanzania using the Demographic Health Survey (DHS) data.
Methods
Study design and sampling strategies
We conducted a cross-sectional secondary study using the 2022 Tanzanian Demographic Health Survey (TDHS) by employing a quantitative approach. The DHS was conducted by the Tanzania NBS and was funded by the United States Agency for International Development(USAID). This process was technically supported by ICF International. The study population included women of reproductive age between 15–49 years old who slept in the household the night before the survey day were included in the survey [22]. This included both usual residents and visitors of the household. The information from the IR file on all members of the households was obtained and weighted to account for national representation and non-response rate. This study analysed information from 15,254 women which was collected during the TDHS of 2022.
This study adopted a two-stage stratified complex sampling method. At first, Tanzania as a country was stratified into two strata, namely Urban and Rural areas. The second stage involved the selection of Primary Sampling Units (PSU) from the sampled strata, in which households were selected for this study. Women from the households were separately interviewed on diverse health issues including demographic characteristics, health services, breast cancer screening, insurance status and other variables which are not included in this study. Since this study used part of the wide TDHS dataset, the detailed methodology is documented in the main document [23, 24]. In addition, the data used in this study were obtained from the DHS website after the research team submitted a concept note and received permission to access the data.
Data analysis
Data from TDHS 2022 were used in this study. In order to account for the complex sampling design and non-response rate among the respondents, all data used during the analysis phase were weighted using individual women’s files (IR using v005/1000,000). Data analysis was conducted using univariate, bivariate and multivariable logistic regression analysis models. Descriptive analysis was conducted to describe the main features of a dataset to provide an overview of the variables. The binary logistic regression analysis model was used to determine the association between breast cancer screening as the dependent variable and independent variables, which are health insurance and other socio-economic factors that were captured in the DHS. This regression analysis represented two models, which are crude and adjusted models. In the crude model, we analysed one independent variable at a time against the dependent variable. The aim was to assess the unadjusted effect of individual factors on the outcome, before adjusting for potential confounders in subsequent models. An adjusted model is a statistical model where the relationship between the dependent variable and multiple independent variables is estimated while controlling for other factors. This model aims to isolate the true effect of the main independent variable(s) of interest from the influence of potential confounding factors. All analyses were performed using Stata version 16 and svy commands were applied in the multivariable logistic regression analysis to account for the complex survey design.
Dependent variables
Breast cancer screening. Women were asked about their screening of the disease status for breast cancer, and responses were yes or no. Which were coded as 1 “yes” and 0 for otherwise.
Independent variables
During analysis, this study measured women’s risk profiles, which refer to the set of socioeconomic and demographic characteristics that may influence their vulnerability to limited healthcare access and adverse health outcomes [25]. Consistent with similar studies on women’s health and healthcare utilization, variables such as age, wealth, education, and marital status were used to construct these profiles [26, 27]. These variables include age categorized into four groups (15–24, 25–34, 35–44, 45 +), wealth index which combined five quantiles (poorest, poorer. middle, richer, richest), marital status in two categories (married and not married), respondent’s current working status by considering the women who are currently engaging in income-generating jobs (Yes/No) and current health insurance coverage (Yes/No). Other variables included visiting a health facility in the past 12 months (Yes/No), education level (no formal education, primary education, secondary education, higher), and getting medical help for oneself: distance to the health facility (not a big problem/a big problem).
Results
Demographic characteristics of the respondents
Among the total respondents, 15,254 included in the study, a total of 15,156 women were asked if they were screened for cervical cancer. Only 4.95% were screened for breast cancer. Most of the women involved in this study were in the age range between 15–34 years old. The majority of the respondents of this study were married (60%) and currently working (59%). Regarding distance to the health facility, about 27% of the interviewed women had a big problem getting medical help and only 6% of the total interviewed women were currently insured. More than half of the respondents (54%) visited health facilities in the last 12 months (Table 1).
Table 1.
Demographic characteristics of the respondents
| Variables | Urban | Rural | Total |
|---|---|---|---|
| Breast cancer screening | |||
| No | 4997(92.09%) | 9439(96.69%) | 14,436(95.05%) |
| Yes | 429(7.91%) | 323(3.31%) | 752(4.95%) |
| Age | |||
| 15–24 | 2105(38.69) | 3747(38.18) | 5852(38.36) |
| 25–34 | 1669(30.67) | 2872(29.27) | 4541(29.77) |
| 35–44 | 1216(22.35) | 2216(22.58) | 3432(22.50) |
| 45 + | 451(8.29) | 978(9.97) | 1429(9.37) |
| Education level | |||
| No formal education | 355(6.52) | 2032(20.71) | 2387(15.65) |
| Primary education | 2384(43.82) | 5029(51.25) | 7413(48.60) |
| Secondary education | 2535(46.59) | 2700(27.51) | 5235(34.32) |
| Higher | 167(3.07) | 52(0.53) | 219(1.44) |
| Marital status | |||
| Not Married | 2622(48.19%) | 3481(35.47%) | 6103(40.01%) |
| Married | 2819(51.81%) | 6332(64.53%) | 9151(59.99%) |
| Working status | |||
| No | 2284(41.98%) | 4047(41.24%) | 6331(41.50%) |
| Yes | 3157(58.02%) | 5766(58.76%) | 8923(58.50%) |
| Wealth Index combined | |||
| Poorest | 76(1.40%) | 2195(22.37%) | 2271(14.89%) |
| Poorer | 124(2.28%) | 2374(24.19%) | 2498(16.38%) |
| Middle | 514(9.45%) | 2549(25.98%) | 3063(20.08%) |
| Richer | 1614(29.66) | 1764(17.98%) | 3378(22.15%) |
| Richest | 3113(57/21%) | 931(9.49%) | 4044(26.51%) |
| Getting medical help for oneself: distance to the health facility | |||
| Big problem | 673(12.37%) | 3384(34.48%) | 4057(26.60%) |
| Not a big problem | 4768(87.63%) | 6429(65.52%) | 11,197(73.4%) |
| Covered by health insurance | |||
| No | 4957(91.10%) | 9429(96.09%) | 14,386(94.31%) |
| Yes | 484(8.90%) | 384(3.91%) | 868(5.69%) |
| Visited a health facility last 12 months | |||
| No | 2434(44.73%) | 4613(47.01%) | 7047(46.20%) |
| Yes | 3007(55.27%) | 5200(52.99%) | 8207(53.80%) |
Table 2 presents the socio-demographic characteristics among insured and uninsured women. About 17% of insured women attended breast cancer screening. Most of the poor were not insured, whereby only 4% of the women who belonged to the poor quantile were insured, while 62% of the women who belonged to the richest quantile were insured.
Table 2.
Socio-demographic characteristics among insured and uninsured women
| Variables | Covered with Health Insurance | ||
|---|---|---|---|
| Uninsured | Insured | Total | |
| Breast cancer screening | |||
| No | 13,664(95.54) | 736(82.99%) | 14,401(94.81%) |
| Yes | 638(4.46%) | 151(17.01%) | 789(5.19%) |
| Age | |||
| 15–24 | 5547(38.61%) | 263(29.58%) | 5810(38.09%) |
| 25–34 | 4336(30.18%) | 273(30.78%) | 4609(30.22%) |
| 35–44 | 3232(22.49%) | 241(27.10%) | 3472(22.76%) |
| 45 + | 1251(8.71%) | 111(12.54%) | 1363(8.93%) |
| Education level | |||
| No formal education | 2411(54.88%) | 39(4.34%) | 2450(16.06%) |
| Primary education | 7885(54.88%) | 239(26.90%) | 8123(53.25%) |
| Secondary education | 3972(27.65%) | 495(55.75%) | 4467(28.29%) |
| Higher | 98(0.68%) | 116(13.01) | 213(1.40%) |
| Marital status | |||
| Not Married | 5631(39.20%) | 371(41.76%) | 6002(39.35%) |
| Married | 8735(60.80%) | 517(58.24%) | 9252(60.65%) |
| Working status | |||
| No | 5865(40.83%) | 317(35.70%) | 6182(40.53%) |
| Yes | 8501(59.17%) | 571(64.30%) | 9072(59.47%) |
| Wealth Index combined | |||
| Poorest | 2430(16.91%) | 36(4.06%) | 2466(16.16%) |
| Poorer | 2513(17.49%) | 66(7.38%) | 2578(16.90%) |
| Middle | 2812(19.57%) | 69(7.75%) | 2880(18.88%) |
| Richer | 3196(22.25%) | 163(18.35%) | 3359(22.02%) |
| Richest | 3416(23.78%) | 554(62.46%) | 3971(26.03%) |
| Getting medical help for oneself: distance to the health facility | |||
| Big problem | 4285(29.83%) | 107(12.09%) | 4393(28.80%) |
| Not a big problem | 10,081(70.17%) | 780(87.91%) | 10,861(71.20%) |
| Type of Residence | |||
| Urban | 4970(34.59%) | 476(53.67%) | 5446(35.70%) |
| Rural | 9397(65.41%) | 411(46.33%) | 9808(64.30%) |
| Visited a health facility last 12 months | |||
| No | 6838(47.59%) | 329(37.11%) | 7167(46.98%) |
| Yes | 7529(52.41%) | 559(62.89%) | 8087(53.02%) |
Regression analysis
The association between health insurance and breast cancer screening
The binary logistic regression analysis was performed to assess factors associated with breast cancer screening, which represents one of the most dangerous non-communicable diseases among women. This regression analysis represented two models which are crude and adjusted models.
Table 3, presents the first logistic regression analysis, whereby models 1 and 2 represent the relationship between breast cancer screening against health insurance coverage and other socio-demographic factors. Health insurance coverage was statistically significant for breast cancer screening. This means that the insured women had higher odds of breast cancer screening than the uninsured women for both the crude model (OR 4.39; p-value < 0.001) and the adjusted model (OR 2.17; p-value < 0.001). The wealth index had a positive influence on attending for screening, whereby odds increased with the increase of the wealth index among women for both crude and adjusted models. Women from rural areas had a lower chance of being screened for breast cancer compared with women from urban areas for both the crude model (OR 0.35; p-value < 0.001) and adjusted model (OR 0.65; p-value < 0.001). Age, education level, current working status and those who visited the health facility at least 12 months had a positive influence on the breast cancer screening for both crude and adjusted models. Marital status and the distance from the health facility had no significant association with breast cancer screening, especially in the adjusted model (p-value was > 0.05).
Table 3.
Bivariable and multivariate analysis on the association between breast cancer screening against health insurance and socio-demographic factors
| Crude Model | Adjusted Model | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| OR | p-value | 95% CI | OR | p-value | 95% CI | ||||
| Breast Cancer Screening | |||||||||
| Age | |||||||||
| 15–24 | 1.00 | 1.00 | |||||||
| 25–34 | 3.04 | < 0.001 | 2.23 | 4.14 | 2.58 | < 0.001 | 1.80 | 3.69 | |
| 35–44 | 5.28 | < 0.001 | 3.98 | 7.00 | 5.12 | < 0.001 | 3.71 | 7.05 | |
| 45 + | 6.39 | < 0.001 | 4.68 | 8.73 | 7.27 | < 0.001 | 5.11 | 10.35 | |
| Education level | |||||||||
| No formal education | 1.00 | ||||||||
| Primary education | 2.63 | < 0.001 | 1.85 | 3.74 | 1.81 | < 0.001 | 1.23 | 2.65 | |
| Secondary education | 3.14 | < 0.001 | 2.16 | 4.57 | 2.34 | < 0.001 | 1.53 | 3.58 | |
| Higher | 14.89 | < 0.001 | 9.48 | 23.39 | 4.08 | < 0.001 | 2.29 | 7.26 | |
| Marital status | |||||||||
| Not Married | 1.00 | ||||||||
| Married | 1.33 | < 0.001 | 1.10 | 1.60 | 0.99 | 0.95 | 0.79 | 1.24 | |
| Type of residence | |||||||||
| Urban | 1.00 | ||||||||
| Rural | 0.35 | < 0.001 | 0.28 | 0.43 | 0.65 | < 0.001 | 0.50 | 0.84 | |
| Working status | |||||||||
| No | 1.00 | ||||||||
| Yes | 2.16 | < 0.001 | 1.70 | 2.73 | 1.31 | 0.03 | 1.03 | 1.67 | |
| Wealth Index combined | |||||||||
| Poorest | 1.00 | ||||||||
| Poorer | 1.72 | < 0.001 | 1.02 | 2.91 | 1.50 | < 0.001 | 0.88 | 2.54 | |
| Middle | 3.24 | < 0.001 | 2.02 | 5.20 | 2.51 | < 0.001 | 1.55 | 4.06 | |
| Richer | 4.84 | < 0.001 | 3.08 | 7.62 | 3.09 | < 0.001 | 1.91 | 5.00 | |
| Richest | 8.57 | < 0.001 | 5.58 | 13.16 | 3.72 | < 0.001 | 2.24 | 6.17 | |
| Getting medical help for oneself: distance to the health facility | |||||||||
| Big problem | 1.00 | ||||||||
| Not a big problem | 1.62 | < 0.001 | 1.28 | 2.05 | 0.95 | 0.67 | 0.74 | 1.22 | |
| Covered by health insurance | |||||||||
| No | 1.00 | ||||||||
| Yes | 4.39 | < 0.001 | 3.46 | 5.57 | 2.17 | < 0.001 | 1.67 | 2.82 | |
| Visited a health facility last 12 months | |||||||||
| No | 1.00 | ||||||||
| Yes | 1.86 | < 0.001 | 1.53 | 2.26 | 1.49 | < 0.001 | 1.20 | 1.84 | |
Key: OR Odds Ratios, CI Confidence Intervals
Discussion
This study analyzed the factors associated with breast screening among insured and uninsured women. The study revealed that insured women had a higher chance of being screened with breast cancer than those who were not insured. This is due to the fact that insured women have improved access to health services compared to the uninsured group. The results are consistent with previous studies, which present health insurance as a bridge to access health services and subsequently improve equity in health services [16, 28–30].
This study found that the wealth index influenced the attendance to breast cancer screening services. This implies that women from the higher quantiles group (richer/richest) had a higher chance of accessing breast cancer screening compared to those from the lower quantiles group (poorer). The results corroborate with previous studies, which indicate that women who are higher income earners have the ability to pay for accessing health services than those who are poor. Therefore, women from higher socio-economic status groups have a higher possibility of accessing health services compared to those with low socio-economic status [31–36].
Women from rural areas had a low chance of being screened for breast cancer compared to those living in urban areas. This implies that women from urban areas had a high chance of being screened for breast cancer as compared to those living in rural areas. This might possibly be explained by the fact that health facilities in urban areas are more available and accessible compared to rural areas. There is also a possibility that urban areas have more advanced facilities which are capable of screening for breast cancer. Physical distance from the health facility and qualified staff are among the factors which influence health service provision, particularly in breast cancer screening. Challenging physical and financial access to health facilities can reduce the utilization of health services among women, in which screening for breast cancer is among the provided services. The results are consistent with previous studies, which indicated the inequalities in access to health services among rural and urban dwellers, whereby women from rural areas face some challenges, including distance and poor provision of health services from the health facilities, compared to those living in urban areas [37–39].
Another finding shows that the chances of a woman being screened for breast cancer increases with increasing age. Women at an advanced age have more chances to visit health facilities for breast cancer screening. The practice of frequent visits to health facilities provides opportunities for screening NCDs such as breast cancer, which are widely available in some routine women wellness clinics. It is evident that women of advanced age might have support from their own family, which might subsequently improve their access to services. The results concur with previous studies, which indicated that, as age increases there is a high possibility of accessing health services due to their income status, marriage, awareness of many diseases which affect women and the need for many health services compared to the young women [39–41].
The level of education among women have an influence on access to health services. Our study presents a correlation between breast cancer screening and education level, showing that as women's education level increases, there is a higher likelihood of being screened for cancer compared to those with lower education levels. The results corroborate with previous studies in which the importance of education on access to health services are more prioritized as educated women are more aware of the diseases compared with those with low education [40, 42, 43]. Further, there is a positive correlation between higher education and socioeconomic status, in which more educated women are more likely to be in middle or higher socio-economic status. Such a situation puts educated women in a more advantageous position in accessing health services compared to women with lower education [38, 43].
Women who are currently working have higher chances of being screened for breast cancer as compared to those who are not currently working. The status of working implies improved socio-economic status, which improves the ability to purchase insurance and pay for health services in particular, screening for health services. The frequency of visits to health services by educated and higher socio-economic women provides the opportunity to gain more information, even for free screening services which are available in the health facilities. The coverage by health insurance might also explain the confidence of women to frequently visit facilities to gain information and subsequently screen for breast cancer, as compared to those who are uninsured. This is consistent with previous studies, which show that, as the socioeconomic condition improves, the possibility of accessing health services, including breast cancer screening also increases [38].
Study strengths and limitations
The strength of this study is the use of the data from DHS which is a national representative of the entire population of women in assessing the influence of health insurance on breast cancer screening by comparing insured and uninsured against other socio-demographic factors in Tanzania. However, this study is limited by the cross-sectional design that does not allow for the study of causal/temporal relationships between socio-economic inequalities in breast cancer screening among insured and uninsured women in Tanzania.
Conclusion
This study highlights the importance of the combination of different factors in breast cancer screening in LMICs. These factors include being an active member of a health insurance scheme, socioeconomic status and education. The government, through Local Government Authorities, to consider these factors during formulation of policy and decision-making to improve access to breast cancer screening among women in Tanzania.
Acknowledgements
The authors acknowledge the DHS custodian for providing access to data which resulted in this study.
Abbreviations
- ICF
Inner City Fund
- BCS
Breast Cancer Screening
- DHS
Demographic Health Survey
- CHF
Community Health Fund
- DHS
Demographic and Health Survey
- iCHF
improved Community Health Fund
- LMICs
Low- and Middle-Income Countries
- NBS
National Bureau of Statistics
- NBS
National Bureau of Statistics
- NHIF
National Health Insurance Fund
- OOPs
Out-of-Pocket
- PSU
Primary Sampling Units
- USAID
United States Agency for International Development
- WHO
World Health Organization
Authors’ contributions
AA, MT, PL and TN participated in the design, analysis and draft of the manuscript. All the authors read and approved the final manuscript.
Funding
Not applicable.
Data availability
Data used in the analysis are freely available to the public and may be used upon request to the DHS custodian ([https://www.dhsprogram.com/](https:/www.dhsprogram.com)).
Declarations
Ethics approval and consent to participate
The study used secondary data hence this study did not require another ethical clearance.
Consent for publication
This manuscript does not contain any individual data so consent for publication is not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data used in the analysis are freely available to the public and may be used upon request to the DHS custodian ([https://www.dhsprogram.com/](https:/www.dhsprogram.com)).